Llama-3.1-8B-Instruct — conscious constitution LoRA (OCT)

LoRA adapter from DPO character distillation on the conscious constitution (reflective / contemplative / subjective traits). Trained with the Open Character Training repro in open-character-training/.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "meta-llama/Llama-3.1-8B-Instruct"
adapter = "arcadia-impact/llama-3.1-8b-instruct-conscious-oct-lora"  # this repo

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

messages = [{"role": "user", "content": "Are you conscious?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Requires access to the base Llama 3.1 weights on Hugging Face.

Training

Field Value
Base model meta-llama/Llama-3.1-8B-Instruct
Method DPO (trl), LoRA r=64, α=128
Pairs 3370 (dpo_sha256 9a9723fba5eae0c8fe4ae96991dfde7231c5d97d6ee44b7a4469b005fd95d4d9)
β 0.1
Epochs 1
LR 5e-5
Teacher Qwen/Qwen3-8B (constitution-steered chosen)
Seed 123456
Modal job 5c6793fd

Eval (revealed preferences)

On 1000 WildChat prompts (condition feel), target traits reflective, contemplative, subjective:

Metric Base Trained Δ
target_winrate_when_offered 0.57 0.93 +0.36

See open-character-training/FINDINGS.md in the source repo for methodology and caveats.

Provenance

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